Method and device for controlling walking assist device

ABSTRACT

A method and device for controlling a walking assist device is disclosed. The method includes determining a first state variable for a gait state of a user wearing the walking assist device based on a gait of the user, obtaining a second state variable which is smoothed and time-delayed from the first state variable, obtaining a third state variable by applying a torque control variable to the second state variable, and determining an assist torque to be provided by the walking assist device based on the obtained third state variable.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims under 35 U.S.C. § 119 to Korean PatentApplication No. 10-2019-0001189 filed on Jan. 4, 2019, in the KoreanIntellectual Property Office, the entire contents of which areincorporated herein by reference in their entirety.

BACKGROUND 1. Field

At least one example embodiment relates to a walking assist deviceand/or a method of controlling the walking assist device.

2. Description of the Related Art

A walking assist device may be used to assist a user who experiencesinconvenience in walking more readily. Such an inconvenience in walkingmay be attributed to various reasons, for example, diseases oraccidents, and the user cannot walk readily on her/his own due to suchreasons. In such a case, the walking assist device may be used to assistthe user during a walking exercise as a part of a rehabilitation. Inaddition, a recent issue of aging societies has contributed to a growingnumber of people who experience inconvenience and pain from reducedmuscular strength or joint problems due to aging. Thus, there is agrowing interest in walking assist devices that enable elderly users orpatients with reduced muscular strength or joint problems to walk withless effort.

A walking assist device may be worn on a body of a user to provide theuser with power needed to walk, and assist the user with walking in anormal gait pattern. The walking assist device may interact directlywith the body of the user, and thus a high level of safety may beneeded.

SUMMARY

At least one example embodiment relates to a method of controlling awalking assist device configured to be worn by a user.

In at least one example embodiment, the method may include determining afirst state variable for a gait phase of the user based on a gait of theuser; obtaining a second state variable by smoothing and time-delayingthe first state variable; obtaining a third state variable by applying atorque control variable to the second state variable; and determining anassist torque provided by the walking assist device based on the thirdstate variable.

In some example embodiments, the method may include determining thetorque control variable in response to the gait phase in a gait cycle ofthe user corresponding to a set gait phase.

In some example embodiments, the method may include smoothing the torquecontrol variable to generate a smoothed torque control variable, whereinthe obtaining of the third state variable includes obtaining the thirdstate variable by applying the smoothed torque control variable to thesecond state variable.

In some example embodiments, in a normal mode, the determining of thetorque control variable includes determining a desired torque controlvariable by applying a state variable value corresponding to a currentgait phase in the gait cycle of the user to a torque control variabledetermining function based on parameters derived from a previouslearning result.

In some example embodiments, in a learning mode, the determining of thetorque control variable includes determining a retrieval torque controlvariable generated based on a probability function, and setting theretrieval torque control variable as the torque control variable.

In some example embodiments, in the learning mode, the determining ofthe torque control variable further includes determining a score of aprevious torque control variable based on a walking assist power indexdetermined from a previous gait of the user; and determining whether thescore satisfies a condition.

In some example embodiments, the walking assist power index includes, afirst walking assist power index indicating a magnitude of walkingassist power transferred by the walking assist device, and a secondwalking assist power index indicating a degree of hindrance to the userin walking by the walking assist device.

In some example embodiments, the determining of the score includesdetermining the score based on a weighted sum of the first walkingassist power index and the second walking assist power index.

In some example embodiments, the determining of the first state variableincludes determining the first state variable based on hip joint angleinformation of the user measured by a sensor of the walking assistdevice.

In some example embodiments, the obtaining of the second state variableincludes smoothing the first state variable to generate a smoothed firststate variable; and obtaining the second state variable by time-delayingthe smoothed first state variable.

In some example embodiments, the smoothing includes obtaining thesmoothed first state variable based on the first state variabledetermined for a current gait cycle and the first state variabledetermined for a previous gait cycle.

Some example embodiments relate to a non-transitory computer-readablemedium comprising computer readable instructions that, when executed bya computer, cause the computer to perform the method of controlling thewalking assist device.

At least one example embodiment relates to a device configured tocontrol a walking assist device.

In at least one example embodiment, the device may include a memory; anda controller configured to control an assist torque provided by thewalking assist device based on a gait of a user wearing the walkingassist device by, determining a first state variable for a gait phase ofthe user based on the gait, obtaining a second state variable bysmoothing and time-delaying the first state variable, obtaining a thirdstate variable by applying a torque control variable to the second statevariable, and determining the assist torque to be provided by thewalking assist device based on the third state variable.

In some example embodiments, the controller is configured to, determinethe gait phase in a gait cycle of the user based on the first statevariable, and determine the torque control variable in response to thegait phase corresponding to a set gait phase.

In some example embodiments, the controller is configured to, obtain thethird state variable by, smoothing the torque control variable togenerate a smoothed torque control variable, and applying the smoothedtorque control variable to the second state variable.

In some example embodiments, the controller is configured to operate ina learning mode to, determine the torque control variable, determine ascore of a previous torque control variable based on a walking assistpower index determined from a previous gait of the user, determine aretrieval torque control variable generated based on a probabilityfunction, in response to the score not satisfying a set condition, andset the retrieval torque control variable as the torque controlvariable.

In some example embodiments, the walking assist power index includes, afirst walking assist power index indicating a magnitude of walkingassist power to be transferred by the walking assist device, and asecond walking assist power index indicating a degree of hindrance tothe user in walking by the walking assist device.

At least one example embodiment relates to a walking assist device.

In at least one example embodiment, the walking assist device mayinclude a sensor configured to measure a gait of a user wearing thewalking assist device; a driver configured to operate an actuator of thewalking assist device to provide an assist torque to the user based on atorque control signal; and a controller configured to generate thetorque control signal to control the assist torque based on the gaitmeasured by the sensor by, determining a first state variable for a gaitphase of the user based on the gait, obtaining a second state variableby smoothing and time-delaying the first state variable, obtaining athird state variable by applying a torque control variable to the secondstate variable, and determining the assist torque provided by thewalking assist device based on the third state variable.

In some example embodiments, the controller is configured to, determinethe gait phase in a gait cycle of the user based on the first statevariable, and determine the torque control variable in response to thegait phase corresponding to a set gait phase.

In some example embodiments, the controller is configured to, obtain thethird state variable by, smoothing the torque control variable togenerate a smoothed torque control variable, and applying the smoothedtorque control variable to the second state variable.

Additional aspects of example embodiments will be set forth in part inthe description which follows and, in part, will be apparent from thedescription, or may be learned by practice of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

These and/or other aspects will become apparent and more readilyappreciated from the following description of example embodiments, takenin conjunction with the accompanying drawings of which:

FIG. 1 is a diagram illustrating a walking assist device worn on a useraccording to at least one example embodiment;

FIG. 2 is a diagram illustrating a configuration of a walking assistdevice according to at least one example embodiment;

FIGS. 3 and 4 are flowcharts illustrating a method of controlling awalking assist device according to at least one example embodiment;

FIG. 5 is a flowchart illustrating a method of determining a torquecontrol variable according to at least one example embodiment;

FIG. 6 is a diagram illustrating a configuration of a device forcontrolling a walking assist device according to at least one exampleembodiment;

FIG. 7 is a diagram illustrating a method of determining a first statevariable according to at least one example embodiment;

FIG. 8 is a diagram illustrating an operation of a state variablesmoother according to at least one example embodiment;

FIG. 9 is a diagram illustrating an operation of a state variabledelayer according to at least one example embodiment;

FIG. 10 is a graph illustrating a relationship between a first statevariable and a gait phase according to at least one example embodiment;

FIG. 11 is a diagram illustrating an operation of a torque controlvariable smoother according to at least one example embodiment;

FIG. 12 is a diagram illustrating a configuration of a system forcontrolling a physical interactive device according to at least oneexample embodiment; and

FIGS. 13A through 13D are diagrams illustrating different examples of asystem for controlling a physical interactive device according to atleast one example embodiment.

DETAILED DESCRIPTION

Hereinafter, some example embodiments will be described in detail withreference to the accompanying drawings. Regarding the reference numeralsassigned to the elements in the drawings, it should be noted that thesame elements will be designated by the same reference numerals,wherever possible, even though they are shown in different drawings.Also, in the description of embodiments, detailed description ofwell-known related structures or functions will be omitted when it isdeemed that such description will cause ambiguous interpretation of thepresent disclosure.

It should be understood, however, that there is no intent to limit thisdisclosure to the particular example embodiments disclosed. On thecontrary, example embodiments are to cover all modifications,equivalents, and alternatives falling within the scope of the exampleembodiments. Like numbers refer to like elements throughout thedescription of the figures.

In addition, terms such as first, second, A, B, (a), (b), and the likemay be used herein to describe components. Each of these terminologiesis not used to define an essence, order or sequence of a correspondingcomponent but used merely to distinguish the corresponding componentfrom other component(s). It should be noted that if it is described inthe specification that one component is “connected,” “coupled,” or“joined” to another component, a third component may be “connected,”“coupled,” and “joined” between the first and second components,although the first component may be directly connected, coupled orjoined to the second component.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting. As used herein, thesingular forms “a,” “an,” and “the,” are intended to include the pluralforms as well, unless the context clearly indicates otherwise. It willbe further understood that the terms “comprises,” “comprising,”“includes,” and/or “including,” when used herein, specify the presenceof stated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

It should also be noted that in some alternative implementations, thefunctions/acts noted may occur out of the order noted in the figures.For example, two figures shown in succession may in fact be executedsubstantially concurrently or may sometimes be executed in the reverseorder, depending upon the functionality/acts involved.

Unless otherwise defined, all terms, including technical and scientificterms, used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which the disclosure of this applicationpertains. Terms, such as those defined in commonly used dictionaries,are to be interpreted as having a meaning that is consistent with theirmeaning in the context of the relevant art, and are not to beinterpreted in an idealized or overly formal sense unless expressly sodefined herein.

Also, in the description of example embodiments, detailed description ofstructures or functions that are thereby known after an understanding ofthe disclosure of the present application will be omitted when it isdeemed that such description will cause ambiguous interpretation of theexample embodiments.

Various example embodiments will now be described more fully withreference to the accompanying drawings in which some example embodimentsare shown. In the drawings, the thicknesses of layers and regions areexaggerated for clarity.

Hereinafter, examples will be described in detail with reference to theaccompanying drawings, and like reference numerals in the drawings referto like elements throughout.

FIG. 1 is a diagram illustrating a walking assist device worn on a useraccording to at least one example embodiment.

A walking assist device 110, or a gait assist device, may assist a user100 wearing the walking assist device 110 in walking readily. Thewalking assist device 110 may assist or support a portion of a leg or anentire leg of the user 100 to help the user 100 walk more readily. Thewalking assist device 110 may be provided in a wearable exoskeleton typeas illustrated in FIG. 1 , and configured to assist or support muscularstrength of the user 100 when the user 100 walks to improve a walkingmovement or a gait of the user 100 or enable the user 100 to walknormally. For example, when a general user or an elderly user withreduced muscular strength wears the walking assist device 110, thewalking assist device 110 may increase a walking ability of the user byenabling the user to walk for a longer period of time as compared towhen the user does not wear the walking assist device 110, and enablethe user to walk independently by providing or adding power needed forthe user to walk. The walking assist device 110 may also be used in suchfields as rehabilitation and walking correction by improving abnormalwalking of a patient. The type of the walking assist device 110illustrated in FIG. 1 is provided as an example, and example embodimentsdescribed herein may thus be applicable to other types of walking assistdevices.

Referring to FIG. 1 , the walking assist device 110 generates a torque,or a rotational force, at left and right hip joints 120L and 120R underthe control of a controller 130, and the generated torque provides legsof the user 100 with power for flexion and extension throughtransferrers 140L and 140R disposed above knees of the user 100. Thewalking assist device 110 measures a walking movement, or a gait, of theuser 100 through a sensor thereof, and estimates a gait state or a gaitphase in a gait cycle of the user 100 based on the measured gait. Thewalking assist device 110 determines a direction in which power is to beprovided to each of the legs and an amount of the power to be provided,at a current point in time, based on the estimated gait phase.

The walking assist device 110 is worn on a body of the user 100 suchthat the walking assist device 110 applies power to the user 100 whilereceiving power applied by the user 100. Thus, there is an interactionbetween the user 100 and the walking assist device 110. In suchinteraction, a reaction to a physical intervention of the walking assistdevice 110 may vary from individual to individual. That is, individualsmay react differently to a same physical intervention.

Thus, one or more example embodiments, may individualize the walkingassist device 110 by determining a rule for adjusting a control variableof the walking assist device 110 to be suitable for each individualuser. The foregoing term “individualization” indicates that a controlpolicy or rule of the walking assist device 110 may be applieddifferently to individual users based on a difference in individual gaitcharacteristic, physical state, muscular strength, and the like. Theindividualization may be achieved by learning the control policy of thewalking assist device 110 based on a measured gait state of a user.

The example embodiments to be described hereinafter provide a safe andconsistent walking assistance performance through the individualizationof the walking assist device 110 based on a state variable and a torquecontrol variable to which a walking movement, or a gait, of a user isapplied. For the safe and consistent walking assistance performance, asmoother and/or a delayer may be used, and reinforcement learning thatimproves (or, alternatively, optimizes) parameters for the controlpolicy may be performed. Through the reinforcement learning, it ispossible to improve assistance transfer power by increasing a rateindicating how much an assist torque provided to the user 100 by thewalking assist device 110 is to be useful for the user 100 in walking,and to reduce a mismatch in walking assistance by decreasing a rateindicating how much assist power provided to the user 100 by the walkingassist device 110 hinders the user 100 in walking. In addition, it ispossible to provide a walking assistance customized (or, alternatively,optimized) for an individual user, reduce an amount of energy needed forwalking, and improve levels of walking regularity, walking safety, andwalking stability. Hereinafter, the example embodiments will bedescribed in detail with reference to the accompanying drawings.

FIG. 2 is a diagram illustrating a configuration of a walking assistdevice according to at least one example embodiment.

Referring to FIG. 2 , a walking assist device 200 includes a sensor 240,a control device 210, and a driver 250. The control device 210 may be adevice for controlling the walking assist device 200. According to anexample, the walking assist device 200 may further include a supportmember to support a body of a user wearing the walking assist device 200and a fixing member to be fastened to the body of the user. For example,the walking assist device 200 may be the walking assist device 110 ofFIG. 1 such that the control device 210 corresponds to the controller130 of FIG. 1 .

The sensor 240 may include various sensors. The sensor 240 may include asensor configured to sense information associated with a walkingmovement, or a gait, of the user, and a sensor configured to senseinformation needed to control an operation of the walking assist device200. For example, the sensor 240 may include a sensor (e.g.,acceleration sensor, inertial sensor, and gyro sensor) configured tomeasure a gait of the user, a torque sensor configured to measure anassist torque transferred by the driver 250, a current/voltage sensor,and the like.

In an example, the gait of the user may be measured through a sensorsensing angle information or motion information of both hip jointscorresponding to positions of the hip joints of both legs of the user.The angle information of the hip joints may include at least one set ofinformation associated with angles of the hip joints, a differencebetween the angles of the hip joints, motion directions of the hipjoints, and angular velocities of the hip joints.

The control device 210 configured to control the walking assist device200 includes a controller 220 and a memory 230. The memory 230 isconnected to the controller 220, and configured to store instructions tobe executed by the controller 220, and data to be processed by thecontroller 220 and/or data having been processed by the controller 220.For example, the memory 230 stores parameters corresponding to an assisttorque control signal output by the controller 220. The memory 230 mayinclude a non-transitory computer-readable storage medium, for example,a high-speed random-access memory (RAM) and/or a nonvolatilecomputer-readable storage medium (e.g., at least one disk storagedevice, flash memory device, or other nonvolatile solid-state memorydevices).

The controller 220 may be implemented using processing circuitry such ashardware including logic circuits, a hardware/software combination suchas a processor executing software; or a combination thereof. Forexample, the processing circuitry may include, but is not limited to, acentral processing unit (CPU), an arithmetic logic unit (ALU), a digitalsignal processor, a microcomputer, a field programmable gate array(FPGA), a System-on-Chip (SoC) a programmable logic unit, amicroprocessor, or an application-specific integrated circuit (ASIC),etc.

The controller 220 generates a control signal to control the walkingassist device 200. For example, the controller 220 generates a torquecontrol signal to control an assist torque to be provided by the walkingassist device 200 based on the gait measured by the sensor 240. Thecontroller 220 controls the driver 250 configured to generate the assisttorque in the walking assist device 200 based on the generated controlsignal.

As discussed in more detail below, the controller 220 may be programmedas a special purpose processor to perform reinforcement learning suchthat the walking assist device 200 may improve assist torque transferpower by increasing a rate at which the assist torque provided to theuser by the walking assist device 200 is useful for the user whenwalking, and reduce the number of mismatches in walking assistance bydecreasing a rate at which the assist torque provided to the user by thewalking assist device 200 hinders the user from walking.

The driver 250 operates an actuator of the walking assist device 200based on the torque control signal generated by the controller 220. Thedriver 250 provides the assist torque to the gait of the hip joints ofthe user through the actuator. The actuator converts electrical energyto kinetic energy, and applies the kinetic energy to the body of theuser to provide the user with power needed for the user to walk. Theactuator is disposed on a portion corresponding to the positions of thehip joints of the user, and generates the assist torque for flexion andextension of the legs of the user to assist the user in walking.

The controller 220 determines a state variable indicating a gait stateof the user based on the gait of the user, and controls the walkingassist device 200 based on the determined state variable. The controller220 sets a parameter to control the assist torque based on the statevariable, and outputs a periodic assist torque control signal to assistthe user in walking based on the set parameter.

In an example, the controller 220 controls an assist torque to beprovided by the walking assist device 200 based on a state variable, anddetermines whether to individualize a control signal used to control theassist torque based on the state variable. The controller 220 sets again to adjust an intensity of the assist torque and sets a time delayto adjust an output time of the assist torque, and defines the statevariable based on the set gain and the set time delay. By adjusting thegain and the time delay, the walking assist device 200 may more stablyrespond to a sudden motion of the user, a sudden stop of the user, or achange in environment, and to improve levels of walking regularity andsafety. A first state variable, a second state variable, and a thirdstate variable to be described hereinafter may be used to define theassist torque control signal that is used to control the operation ofthe walking assist device 200.

In an example, the controller 220 determines a first state variable fora gait state of the user based on a walking movement, or a gait, of theuser. The controller 220 smooths the first state variable andtime-delays the smoothed first state variable to obtain a second statevariable which is smoothed and time-delayed from the first statevariable. In this example, the term “time-delay” may indicate a processperformed using a time delay which is a time value set (or,alternatively, preset) by the user or based on a control design rule.For example, when a walking speed of the user is greater than a firstreference value, the controller 220 sets a time delay to be relativelysmall. Conversely, when a walking speed of the user is less than thefirst reference value, the controller 220 sets a time delay to berelatively large. For another example, the controller 220 determines adelay based on a walking acceleration of the user. In this example, whena walking acceleration of the user is greater than a second referencevalue, the controller 220 sets a time delay to be small. Conversely,when a walking acceleration of the user is less than the secondreference value, the controller 220 sets a time delay to be large. Inthe foregoing examples, the walking speed and the walking accelerationof the user may be determined based on sensing information obtained fromthe sensor 240.

The controller 220 obtains a third state variable by applying a torquecontrol variable to the second state variable. The controller 220determines a gait phase in a gait cycle of the user based on the firststate variable, and determines the torque control variable when thedetermined gait phase corresponds to a defined or (alternatively, apredefined) gait phase.

The torque control variable is determined in different ways in alearning mode and in a normal mode. The user may select the learningmode or the normal mode. The user may select the learning mode tooptimize or individualize the walking assist device 200. Through thelearning mode, parameters associated with controlling an assist torqueto be provided by the walking assist device 200 may be adjusted based ona gait state or a gait style of the user.

When the normal mode is selected, the controller 220 determines adesired (or, alternatively, an optimal) torque control variable byapplying a state variable value corresponding to a current gait phase ofthe user to a torque control variable determining function based onparameters derived from a previous learning result. The controller 220determines the determined torque control variable to be the torquecontrol variable to be applied to the second state variable.

When the learning mode is selected, the controller 220 determines, to bethe torque control variable, a retrieval torque control variablegenerated based on a probability function. In an example, in thelearning mode, the controller 220 determines a score of a previoustorque control variable based on a walking assist power index determinedfrom a previous gait of the user. The walking assist power indexincludes a first walking assist power index indicating a magnitude ofwalking assistance power transferred by the walking assist device 200,and a second walking assist power index indicating a degree of hindranceto the user in walking by the walking assist device 200. The firstwalking assist power index is a positive measurement result value forwalking assistance, and the second walking assist power index is anegative measurement result value for walking assistance. For example,the controller 220 calculates the score in a form of weighted sum of thefirst walking assist power index and the second walking assist powerindex.

The walking assist power index may be calculated in real time, and acontrol policy of the walking assist device 200 may be learned based onthe walking assist power index. In the learning mode, a parameter of theprobability function may be determined such that the first walkingassist power index increases and the second walking assist power indexdecreases, and the retrieval torque control variable may be determinedbased on the probability function. When the score determined based onthe walking assist power index does not satisfy a set (or,alternatively, a preset) condition, the controller 220 generates theretrieval torque control variable based on the probability function, anddetermines the generated retrieval torque control variable to be thetorque control variable to be applied to the second state variable. Theset (or, alternatively, preset) condition may indicate that the score isgreater than a threshold value, or the number of times calculating thescore reaches a certain number of times.

The controller 220 obtains a third state variable by smoothing thedetermined torque control variable and applying the smoothed torquecontrol variable to the second state variable. The controller 220generates a control signal to determine the assist torque to be providedby the walking assist device 200 based on the obtained third statevariable. The controller 220 generates an assist torque control signalby applying, to the third state variable, a gain for adjusting anintensity of the assist torque, and controls the assist torque to beprovided by the walking assist device 200 based on the generated assisttorque control signal.

Through the operations described above, it is possible to reduce thenumber of mismatches in walking assistance provided by the walkingassist device 200, improve an assist torque transfer efficiency, and/orimprove a level of walking regularity. For example, throughreinforcement learning, the walking assist device 200 may improve assisttorque transfer power by increasing a rate at which the assist torqueprovided to the user by the walking assist device 200 is useful for theuser when walking, and reduce the number of mismatches in walkingassistance by decreasing a rate at which the assist torque provided tothe user by the walking assist device 200 hinders the user from walking.

According to an example, a remote controller (not shown) configured toremotely control the walking assist device 200 may be provided. Theremote controller may control an overall operation of the walking assistdevice 200 in response to an input from the user. For example, theremote controller may initiate or suspend the operation of the walkingassist device 200. In addition, the remote controller may generate anassist torque control signal to control a walking assistance operationof the walking assist device 200, and transmit the generated assisttorque control signal to the walking assist device 200. The remotecontroller may provide a user interface (UI) that facilitatesmanipulation or operation of the walking assist device 200. Through theUI, the user may directly set a gain associated with an intensity of anassist torque to be provided by the walking assist device 200 or a delayin an output time of the assist torque.

FIGS. 3 and 4 are flowcharts illustrating a method of controlling awalking assist device according to at least one example embodiment. Themethod of controlling a walking assist device will be hereinafter simplyreferred to as a control method, and the control method may be performedby a device for controlling a walking assist device which will behereinafter simply referred to as a control device, which may be, forexample, the control device 210.

Referring to FIG. 3 , in operation 310, a control device determines afirst state variable for a gait state of a user wearing a walking assistdevice based on a gait of the user. For example, the control devicedetermines the first state variable based on hip joint angle informationof the user that is measured by a sensor of the walking assist device.The first state variable may indicate a gait or a movement of the userin association with the gait state of the user, and may be defined byangle information of left and right hip joints as represented byEquation 1.y ₂(t)=sin q _(r)(t)−sin q _(l)(t)  [Equation 1]

In Equation 1, y₂(t) denotes a first state variable based on time t.q_(r)(t) and q_(l)(t) denote an angle of a right hip joint and an angleof a left hip joint, respectively, based on time t.

In operation 320, the control device obtains a second state variablewhich is smoothed and time-delayed from the first state variable.Operation 320 may be performed in detail as described hereinafter withreference to FIG. 4 . Referring to FIG. 4 , in operation 410, thecontrol device smooths the first state variable. Through the smoothing,noise in the first state variable may be reduced. The control device maysmooth the first state variable using a smoothing filter including, forexample, a low-pass filter (LPF). The control device may obtain thesmoothed first state variable by performing low-pass filtering based ona first state variable determined for a current gait cycle and a firststate variable determined for a previous gait cycle, as represented byEquation 2.y=(1−α)y _(prev) +αy _(raw),(0≤α≤1)  [Equation 2]

In Equation 2, y denotes a smoothed first state variable, and α denotesa defined (or, alternatively, a predefined) constant having a valuebetween 0 and 1. y_(prev) denotes a first state variable determined fora previous gait cycle, and y_(raw) denotes a first state variabledetermined for a current gait cycle.

In operation 420, the control device obtains the second state variableby time-delaying the smoothed first state variable obtained in operation410. In another example, the time-delaying may be performed prior to thesmoothing. In this example, the second state variable may be obtained byperforming the smoothing, such as, for example, filtering, on thetime-delayed first state variable obtained through the time-delaying. Inthe time-delaying process, a time delay value applied to the first statevariable may be a defined (or, alternatively, a predefined) value or avalue set by the user.

In operation 430, the control device determines whether a gait phase ofthe user corresponds to a defined (or, alternatively, a predefined) gaitphase. The control device calculates a current gait phase based on thesmoothed first state variable obtained in operation 410 as representedby Equation 3.

$\begin{matrix}{{\phi = {\frac{1}{2\pi}\left( {{{atan}\; 2\left( {{cy},\overset{.}{y}} \right)} - \pi} \right)}},{c = 6}} & \left\lbrack {{Equation}\mspace{14mu} 3} \right\rbrack\end{matrix}$

In Equation 3, ϕ denotes a gait phase, and y denotes a smoothed firststate variable. The calculated gait phase may be periodic based on agait of the user.

In operation 440, when the gait phase of the user corresponds to thedefined (or, alternatively, a predefined) gait phase, the control devicedetermines a torque control variable. The torque control variable may beused to more finely adjust a point in time at which an assist torquecontrol signal is to be applied. In a mode which is not a learning mode,a previously determined torque control variable or a defined (or,alternatively, a predefined) torque control variable may be used.However, in the learning mode, a desired (or, alternatively, an optimal)torque control variable may be retrieved based on a gait of the user.The learning mode may be a process for individualizing the walkingassist device to be suitable for the user, and whether to enter thelearning mode or not may be selected by the user. Hereinafter, how todetermine a torque control variable will be described in detail withreference to FIG. 5 .

In operation 450, the control device smooths the torque control variabledetermined in operation 440. In an example, the torque control variabledetermined in operation 440 may change discontinuously and such adiscontinuous change of the torque control variable may generate arelatively great jerk motion, which may pose a threat to the safety ofthe user and the walking assist device. Thus, a consistent reaction maynot be readily derived from the user. To inhibit (or, alternatively,prevent) this, the control device may smooth the torque control variablesuch that the torque control variable is gradually applied. By thesmoothed torque control variable, an assist torque to be provided to thewalking assist device may continuously and smoothly change. Thus, thejerk motion may not be generated in the walking assist device, and safeand stable interactive learning may occur between the user and thewalking assist device.

When the gait phase of the user does not correspond to the defined (or,alternatively, a predefined) gait phase as a result of the determinationin operation 430, the torque control variable applied to a previouscycle may be applied unchanged to a current cycle.

Referring back to FIG. 3 , in operation 330, the control device obtainsa third state variable by applying the torque control variable to thesecond state variable. In operation 340, the control device determinesan assist torque to be provided by the walking assist device based onthe obtained third state variable. The control device generates anassist torque control signal by applying, to the third state variable, again for adjusting an intensity of the assist torque, and controls theassist torque to be provided by the walking assist device based on thegenerated assist torque control signal. The assist torque controlled insuch a manner is applied during one gait cycle of the user. When thegait cycle is terminated, operations 310 through 340 are performed againon a next gait cycle.

According to at least one example embodiment, it is possible toeffectively individualize a walking assist device to be suitable to auser of the walking assist device based on a gait characteristic of theuser, and to improve safety of both the user and the walking assistdevice by smoothly controlling an assist torque to be provided by thewalking assist device.

FIG. 5 is a flowchart illustrating a method of determining a torquecontrol variable according to at least one example embodiment.

Referring to FIG. 5 , when determining the torque control variable inoperation 440, the controller may perform operations 510 to 560,discussed below.

In operation 510, a control device determines whether a current set modeis a learning mode. In an example, a user may adjust a mode of a walkingassist device to be the learning mode or a normal mode.

In operation 520, when the current set mode is the normal mode, thecontrol device determines a desired (or, alternatively, an optimal)torque control variable without a learning process. For example, thecontrol device determines the desired (or, alternatively, optimal)torque control variable by applying a state variable value correspondingto a current gait phase of the user to a torque control variabledetermining function based on parameters derived from a previouslearning result. In an example, when learning is already completed andthe normal mode operates currently, the control device sets a controlpolicy suitable to a current gait state of the user using parametersobtained from a previous learning process, and determines the desired(or, alternatively, optimal) torque control variable using the setcontrol policy. In another example, the control device uses a previouslydetermined optimal torque control variable. The control devicedetermines the determined torque control variable to be a torque controlvariable to be applied to a second state variable.

When the current set mode is the learning mode, the control deviceperforms reinforcement learning including operations 530 through 560.Such a learning process may be performed once per gait cycle of theuser.

In operation 530, the control device determines a score, for example, areward, for a previous torque control variable based on a walking assistpower index determined for a previous gait of the user. The walkingassist power index includes a first walking assist power indexindicating a magnitude of walking assistance power transferred from thewalking assist device, and a second walking assist power indexindicating a degree of hindrance to the user in walking by the walkingassist device. In an example, the first walking assist power indexindicates how assist power is well transferred to the user for walking.The greater the first walking assist power index the greater the assistpower to be transferred. In addition, the second walking assist powerindex indicates a quantity of hindrance to the user in walking and isindicated by a negative value. The smaller the value the greater theresistance of the hindrance to the user in walking.

In an example, the score may be determined based on a weighted sum ofthe first walking assist power index and the second walking assist powerindex as represented by Equation 4.Score (reward)=0.25MPP+5MNP  [Equation 4]

In Equation 4, MPP (Mean Positive Power) and MNP (Mean Negative Power)denote a first walking assist power index and a second walking assistpower index, respectively. In Equation 4, weights applied to MPP and MNPare provided as examples, and the weights may vary according to anexample. For example, when one cycle of learning is applied per gaitcycle, MPP and MNP indicate a mean value of the first walking assistpower index and a mean value of the second walking assist power index,respectively, for a gait during a previous gait cycle, for example, twosteps or one stride. Equation 4 above may be an objective function foradjusting a learning result to be desirable.

When the score represented by the weighted sum of the first walkingassist power index and the second walking assist power index increases,the walking assistance to be provided may be smoother, without hindranceto the user in walking while naturally great assist power is beingprovided. The learning process may be performed in accordance with acurrent gait state of the user. For example, the learning process may beperformed such that a value of the first walking assist power indexincreases while an absolute value of the second walking assist powerindex does not excessively increase.

In operation 540, the control device determines whether a terminationcondition is satisfied. The termination condition indicates whether thescore determined in operation 530 exceeds a threshold value, or whetherthe number of repetitions of the learning process reaches a defined (or,alternatively, a predefined) number of times. In operation 550, when thetermination condition is not satisfied, the control device updates aparameter of a probability model for determining a retrieval torquecontrol variable. The control device updates the parameter of theprobability model such that the score increases. The updating of theparameter may be performed repetitively on each gait cycle until thetermination condition is satisfied.

In operation 560, the control device generates the retrieval torquecontrol variable based on a probability model-based function. Thecontrol device generates the retrieval torque control variable based onthe probability model-based function, and determines the generatedretrieval torque control variable to be the torque control variable tobe applied to the second state variable.

A score for a retrieval torque control variable newly determined in eachlearning process may be measured, and a parameter of the probabilitymodel may be updated repetitively until the termination condition issatisfied.

FIG. 6 is a diagram illustrating a configuration of a control device forcontrolling a walking assist device according to at least one exampleembodiment.

Referring to FIG. 6 , a control device 610 of a walking assist device600 may correspond to the control device 210 of FIG. 2 .

The control device 610 may include a state variable smoother 620, astate variable delayer 630, a learner 640, a torque control variablesmoother 650, and a torque control signal generator 660.

For example, as discussed in more detail below, the processing circuitryincluded in the control device 610 may be configured as a specialpurpose processor to perform the operations of the state variablesmoother 620, the state variable delayer 630, the learner 640, thetorque control variable smoother 650, and the torque control signalgenerator 660.

The control device 610 may use such components to safely and effectivelyindividualize walking assistance to be suitable to a user of the walkingassist device 600, and train or learn a control policy to be applied tothe walking assist device 600. In an example, operations of the statevariable smoother 620, the state variable delayer 630, the learner 640,the torque control variable smoother 650, and the torque control signalgenerator 660 may be performed by the controller 220 described abovewith reference to FIG. 2 .

A first state variable for a gait state of the user is defined based oninformation associated with a gait of the user measured by a sensor ofthe walking assist device 600. Referring to FIG. 7 , the sensor of thewalking assist device 600 senses a left hip joint angle qt and a righthip joint angle q_(r) associated with a current gait of the user. To thecontrol device 610, left hip joint angle information and right hip jointangle information sensed by the sensor are transferred. The controldevice 610 then determines the first state variable based on the lefthip joint angle information and the right hip joint angle information.For example, the control device 610 calculates the first state variablebased on the left hip joint angle information and the right hip jointangle information as represented by Equation 1 above.

Referring back to FIG. 6 , the state variable smoother 620 smooths thefirst state variable determined based on the sensed hip joint angleinformation to change the first state variable to a smoother signal.Such a smoothing process may include, for example, low-pass filtering asrepresented by Equation 2 above. Referring to FIG. 8 , a first statevariable which includes noise or is not smooth is input to the statevariable smother 620, and the state variable smoother 620 changes theinput first state variable to a smoother signal and outputs the smoothersignal using a filter.

Referring back to FIG. 6 , the state variable delayer 630 time-delaysthe smoothed first state variable, and outputs a second state variableas a result of performing the time-delaying. The second state variablecorresponds to a result of smoothing the first state variable calculatedas represented by Equation 1 above and then time-delaying the smoothedfirst state variable. Referring to FIG. 9 , a smoothed first statevariable y(t) is input to the state variable delayer 630, and the statevariable delayer 630 applies a time delay Δt to the input first statevariable y(t) and outputs a second state variable y(t−Δt). The timedelay Δt determines an output time of a increased (or, alternatively, amaximum) assist torque and may be a value set by the user, for example,a constant.

Referring back to FIG. 6 , the learner 640 estimates a current gaitphase of the user based on the smoothed first state variable. Forexample, the learner 640 estimates a gait phase based on Equation 5.

$\begin{matrix}{{phase} = {\frac{1}{2\pi}{atan}\; 2\left( {{{cy}\left( {t - {\Delta\; t}} \right)},{\overset{.}{y}\left( {t - {\Delta\; t}} \right)}} \right)}} & \left\lbrack {{Equation}\mspace{14mu} 5} \right\rbrack\end{matrix}$

In Equation 5, phase denotes a gait phase of a user, and y(t) denotes asmoothed first state variable. Δt denotes a time delay, and c denotes adefined (or, alternatively, a predefined) constant as a scaling factorfor scaling. An example of a change of the gait phase calculated basedon Equation 5 above over time will be described hereinafter withreference to FIG. 10 .

FIG. 10 illustrates a graph indicating a change of a smoothed firststate variable 1010 over time and a change of a gait phase 1020determined based on the first state variable 1010 over time. Both thefirst state variable 1010 and the gait phase 1020 have periodicity.

Referring back to FIG. 6 , the learner 640 determines whether a currentgait phase corresponds to a defined (or, alternatively, a predefined)gait phase. When the current gait phase corresponds to the defined (or,alternatively, a predefined) gait phase, the learner 640 determines atorque control variable. When a set mode is a learning mode, the learner640 retrieves a torque control variable to improve (or, alternatively,optimize) a point in time at which an assist torque control signal is tobe applied by performing reinforcement learning. When the reinforcementlearning is performed, the learner 640 calculates a score based on afirst walking assist power index and a second walking assist powerindex. The learner 640 determines parameters of a probabilitymodel-based function based on the calculated score and the first statevariable, and generates a retrieval torque control variable based on theprobability model-based function. The retrieval torque control variablemay be used to finely adjust a time delay applied by the state variabledelayer 630. Herein, a learning process may be a process of finelyadjusting the time delay based on the retrieval torque control variable,and discovering the retrieval torque control variable such that thescore calculated as a result of the adjusting is increased (or,alternatively, maximized). Such learning process may be performed on acertain gait phase.

In a normal mode which is not the learning mode, the learner 640determines a desired (or, alternatively, an optimal) torque controlvariable without performing the learning process. For example, thelearner 640 determines the desired (or, alternatively, optimal) torquecontrol variable by applying a state variable value corresponding to acurrent gait phase of the user to a torque control variable determiningfunction based on parameters derived from a previous learning result.The desired (or, alternatively, optimal) torque control variable mayalso be used to finely adjust a time delay applied by the state variabledelayer 630.

The torque control variable smoother 650 smooths the retrieval torquecontrol variable or the optimal torque control variable. Referring toFIG. 11 , the torque control variable smoother 650 performs suchsmoothing process such that a torque control variable changes graduallyaround a time point to which the torque control variable is applied andaround a time point in which the application is terminated. Thus, it ispossible to inhibit (or, alternatively prevent) an assist torqueprovided by the walking assist device 600 from rapidly changing.

Referring back to FIG. 6 , the torque control variable smoother 650generates a third state variable by applying the smoothed torque controlvariable to the second state variable output from the state variabledelayer 630, and outputs the generated third state variable. The torquecontrol signal generator 660 generates an assist torque control signalbased on the third state variable, and transmits the generated assisttorque control signal to the walking assist device 600. The torquecontrol signal generator 660 generates the assist torque control signalby applying, to the third state variable, a gain for adjusting anintensity of the assist torque. The assist torque control signal may bemaintained for one gait cycle of the user. In an example, the torquecontrol signal generator 660 inverts the assist torque control signal togenerate the inverted assist torque control signal. The assist torquecontrol signal may be applied to assist a left leg of the user withwalking, and the inverted assist torque control signal may be applied toassist a right leg of the user with walking.

FIG. 12 is a diagram illustrating a configuration of a system forcontrolling a physical interactive device according to at least oneexample embodiment. The system for controlling a physical interactivedevice will be hereinafter simply referred to as a control system.

Referring to FIG. 12 , the example embodiments described above withreference to FIGS. 1 through 11 are expandable and applicable to controla physical interactive device 1200 which is physically interactive withhumans. The physical interactive device 1200 includes, for example, awheel-based mobile robot, an articulated robotic limb, a biped walkinghumanoid robot, and the like.

When there is a physical interaction between a user and the physicalinteractive device 1200, a control device 1210 configured to control thephysical interactive device 1200 may smooth or intentionally time-delaya control signal used to control the physical interactive device 1200for the safety of the user and the physical interactive device 1200.Similar to the control device 610 of the walking assist device 600illustrated in FIG. 6 , the control device 1210 includes a statevariable smoother 1220, a state variable delayer 1230, a learner 1240, apower control variable smoother 1250, and a power control signalgenerator 1260. Respective operations of such components of the controldevice 1210 respectively correspond to the operations of the componentsof the control device 610, and thus detailed descriptions of theoperations will be omitted here for brevity. However, the control device1210 of FIG. 12 may differ from the control device 610 of FIG. 6 in thatthe control device 1210 generates a power control signal to control adriver of the physical interactive device 1200 through the power controlsignal generator 1260, whereas the control device 610 generates anassist torque control signal to control an assist torque of the walkingassist device 600.

The learner 1240 determines whether a defined (or, alternatively, apredefined) event occurs based on information measured by a sensor ofthe physical interactive device 1200, and performs a learning process inresponse to the event occurring. For example, when there is a user-robotmutual contact or interaction, the learner 1240 learns a user-customizedcontrol operation of learning a reaction that is most preferred by theuser, for example. A method of calculating a state variable, a score,and the like to be used for the learning may vary as described in theexample of a walking assist device. In addition, the learner 1240 mayperform the learning process using other optimization or learningalgorithms, such as, for example, evolutionary computation, in additionto reinforcement learning.

As described above, the control device 1210 may facilitate a smootherand safer action-reaction interaction between the user and the physicalinteractive device 1200.

FIGS. 13A through 13D are diagrams illustrating different examples of acontrol system for controlling a physical interactive device accordingto at least one example embodiment.

Referring to FIGS. 13A through 13D, a control system for controlling aphysical interactive device 1300 may be embodied by selectivecombinations of the physical interactive device 1300, a state variablesmoother 1310, a state variable delayer 1320, a learner 1330, a powercontrol variable smoother 1340, and a power control variable delayer1350. In addition, a connection of components or elements in eachselective combination may be embodied in various ways.

The units and/or modules described herein may be implemented usinghardware components and software components. For example, the hardwarecomponents may include microphones, amplifiers, band-pass filters, audioto digital convertors, and processing devices. A processing device maybe implemented using one or more hardware device configured to carry outand/or execute program code by performing arithmetical, logical, andinput/output operations. The processing device(s) may include aprocessor, a controller and an arithmetic logic unit, a digital signalprocessor, a microcomputer, a field programmable array, a programmablelogic unit, a microprocessor or any other device capable of respondingto and executing instructions in a defined manner. The processing devicemay run an operating system (OS) and one or more software applicationsthat run on the OS. The processing device also may access, store,manipulate, process, and create data in response to execution of thesoftware. For purpose of simplicity, the description of a processingdevice is used as singular; however, one skilled in the art willappreciated that a processing device may include multiple processingelements and multiple types of processing elements. For example, aprocessing device may include multiple processors or a processor and acontroller. In addition, different processing configurations arepossible, such a parallel processors.

The software may include a computer program, a piece of code, aninstruction, or some combination thereof, to independently orcollectively instruct and/or configure the processing device to operateas desired, thereby transforming the processing device into a specialpurpose processor. Software and data may be embodied permanently ortemporarily in any type of machine, component, physical or virtualequipment, computer storage medium or device, or in a propagated signalwave capable of providing instructions or data to or being interpretedby the processing device. The software also may be distributed overnetwork coupled computer systems so that the software is stored andexecuted in a distributed fashion. The software and data may be storedby one or more non-transitory computer readable recording mediums.

The methods according to the above-described example embodiments may berecorded in non-transitory computer-readable media including programinstructions to implement various operations of the above-describedexample embodiments. The media may also include, alone or in combinationwith the program instructions, data files, data structures, and thelike. The program instructions recorded on the media may be thosespecially designed and constructed for the purposes of exampleembodiments, or they may be of the kind well-known and available tothose having skill in the computer software arts. Examples ofnon-transitory computer-readable media include magnetic media such ashard disks, floppy disks, and magnetic tape; optical media such asCD-ROM discs, DVDs, and/or Blue-ray discs; magneto-optical media such asoptical discs; and hardware devices that are specially configured tostore and perform program instructions, such as read-only memory (ROM),random access memory (RAM), flash memory (e.g., USB flash drives, memorycards, memory sticks, etc.), and the like. Examples of programinstructions include both machine code, such as produced by a compiler,and files containing higher level code that may be executed by thecomputer using an interpreter. The above-described devices may beconfigured to act as one or more software modules in order to performthe operations of the above-described example embodiments, or viceversa.

A number of example embodiments have been described above. Nevertheless,it should be understood that various modifications may be made to theseexample embodiments. For example, suitable results may be achieved ifthe described techniques are performed in a different order and/or ifcomponents in a described system, architecture, device, or circuit arecombined in a different manner and/or replaced or supplemented by othercomponents or their equivalents. Accordingly, other implementations arewithin the scope of the following claims.

What is claimed is:
 1. A method of controlling a walking assist device,the walking assist device configured to be worn by a user, the methodcomprising; determining a first state variable for a gait phase of theuser based on a gait of the user; obtaining a second state variable bysmoothing and time-delaying the first state variable; determining atorque control variable based on the gait phase in a gait cycle of theuser; obtaining a third state variable by applying the torque controlvariable to the second state variable; and determining an assist torqueprovided by the walking assist device based on the third state variable,wherein, in a learning mode for individualizing the walking assistdevice to be suitable for the user, the determining of the torquecontrol variable comprises updating a parameter associated withcontrolling the assist torque to be provided by the walking assistdevice by selectively adjusting the torque control variable during a setgait phase of each gait cycle by (i) determining a walking assistancepower index indicating an efficiency of the assist torque provided bythe walking assist device to the user during a previous gait cycle and,(ii) adjusting the torque control variable associated with a currentgait cycle based on the walking assistance power index, and wherein, ina normal mode, the determining of the torque control variable comprisesdetermining the torque control variable based on the updated parameterderived from a learning result of the learning mode of the walkingassist device.
 2. The method of claim 1, further comprising: smoothingthe torque control variable to generate a smoothed torque controlvariable, wherein the obtaining of the third state variable includesobtaining the third state variable by applying the smoothed torquecontrol variable to the second state variable.
 3. The method of claim 1,wherein, in the normal mode, the determining of the torque controlvariable comprises: determining a desired torque control variable byapplying a state variable value corresponding to a current gait phase inthe gait cycle of the user to a torque control variable determiningfunction based on the parameter derived from the previous learningresult.
 4. The method of claim 1, wherein, in the learning mode, thedetermining of the torque control variable comprises: determining aretrieval torque control variable generated based on a probabilityfunction, and setting the retrieval torque control variable as thetorque control variable.
 5. The method of claim 4, wherein, in thelearning mode, the determining of the torque control variable furthercomprises: determining a score of a previous torque control variablebased on the walking assist power index determined from a previous gaitof the user; and determining whether the score satisfies a condition. 6.The method of claim 5, wherein the walking assist power index includes,a first walking assist power index indicating a magnitude of walkingassist power transferred by the walking assist device, and a secondwalking assist power index indicating a degree of hindrance to the userin walking by the walking assist device; and the torque control variableis selectively adjusted based on the score by increasing the firstwalking assist power index in view of maintaining an absolute value ofthe second walking assist power index below a threshold.
 7. The methodof claim 6, wherein the determining of the score comprises: determiningthe score based on a weighted sum of the first walking assist powerindex and the second walking assist power index.
 8. The method of claim1, wherein the determining of the first state variable comprises:determining the first state variable based on hip joint angleinformation of the user measured by a sensor of the walking assistdevice.
 9. The method of claim 1, wherein the first state variable is adiscontinuous first state variable due to noise present therein, and theobtaining of the second state variable comprises: smoothing the firststate variable by removing the noise from the discontinuous first statevariable to generate a continuous first state variable; and obtainingthe second state variable by time-delaying the continuous first statevariable by a time delay that varies directly with a speed oracceleration of the user.
 10. The method of claim 9, wherein thesmoothing comprises: generating the continuous first state variablebased on the first state variable determined for a current gait and thefirst state variable determined for a previous gait.
 11. Anon-transitory computer-readable medium comprising computer readableinstructions that, when executed by a computer, cause the computer toperform the method of claim
 1. 12. A device configured to control awalking assist device, the device comprising: a memory; and a controllerconfigured to control an assist torque provided by the walking assistdevice based on a gait of a user wearing the walking assist device by,determining a first state variable for a gait phase of the user based onthe gait, obtaining a second state variable by smoothing andtime-delaying the first state variable, determining a torque controlvariable based on the gait phase in a gait cycle of the user, obtaininga third state variable by applying the torque control variable to thesecond state variable, and determining the assist torque to be providedby the walking assist device based on the third state variable, wherein,in a learning mode for individualizing the walking assist device to besuitable for the user, the determining of the torque control variablecomprises updating a parameter associated with controlling the assisttorque to be provided by the walking assist device by selectivelyadjusting the torque control variable during a set gait phase of eachgait cycle by (i) determining a walking assistance power indexindicating an efficiency of the assist torque provided by the walkingassist device to the user during a previous gait cycle, and (ii)adjusting the torque control variable associated with a current gaitcycle based on the walking assistance power index, and wherein, in anormal mode, the determining of the torque control variable comprisesdetermining the torque control variable based on the updated parameterderived from a learning result of the learning mode of the walkingassist device.
 13. The device of claim 12, wherein the controller isconfigured to, determine the gait phase in the gait cycle of the userbased on the first state variable.
 14. The device of claim 13, whereinthe controller is configured to, obtain the third state variable by,smoothing the torque control variable to generate a smoothed torquecontrol variable, and applying the smoothed torque control variable tothe second state variable.
 15. The device of claim 13, wherein thecontroller is configured to operate in the learning mode to, determinethe torque control variable, determine a score of a previous torquecontrol variable based on the walking assist power index determined froma previous gait of the user, determine a retrieval torque controlvariable generated based on a probability function, in response to thescore not satisfying a set condition, and set the retrieval torquecontrol variable as the torque control variable.
 16. The device of claim15, wherein the walking assist power index includes, a first walkingassist power index indicating a magnitude of walking assist power to betransferred by the walking assist device, and a second walking assistpower index indicating a degree of hindrance to the user in walking bythe walking assist device; and the controller is configured to adjustthe torque control variable based on the score by increasing the firstwalking assist power index in view of maintaining an absolute value ofthe second walking assist power index below a threshold.
 17. A walkingassist device comprising: a sensor configured to measure a gait of auser wearing the walking assist device; a driver configured to operatean actuator of the walking assist device to provide an assist torque tothe user based on a torque control signal; and a controller configuredto generate the torque control signal to control the assist torque basedon the gait measured by the sensor by, determining a first statevariable for a gait phase of the user based on the gait, obtaining asecond state variable by smoothing and time-delaying the first statevariable, determining a torque control variable based on the gait phasein a gait cycle of the user, obtaining a third state variable byapplying the torque control variable to the second state variable, anddetermining the assist torque provided by the walking assist devicebased on the third state variable, wherein, in a learning mode forindividualizing the walking assist device to be suitable for the user,the determining of the torque control variable comprises updating aparameter associated with controlling the assist torque to be providedby the walking assist device by selectively adjusting the torque controlvariable during a set gait phase of each gait cycle by (i) determining awalking assistance power index indicating an efficiency of the assisttorque provided by the walking assist device to the user during aprevious gait cycle, and (ii) adjusting the torque control variableassociated with a current gait cycle based on the walking assistancepower index, and wherein, in a normal mode, the determining of thetorque control variable comprises determining the torque controlvariable based on the updated parameter derived from a learning resultof the learning mode of the walking assist device.
 18. The walkingassist device of claim 17, wherein the controller is configured todetermine the gait phase in the gait cycle of the user based on thefirst state variable.
 19. The walking assist device of claim 18, whereinthe controller is configured to, obtain the third state variable by,smoothing the torque control variable to generate a smoothed torquecontrol variable, and applying the smoothed torque control variable tothe second state variable.